Overview

Dataset statistics

Number of variables11
Number of observations1231
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory115.4 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

Opp Pass TDs Allowed is highly overall correlated with Yards/Pass Attempt Allowed and 1 other fieldsHigh correlation
Yards/Pass Attempt Allowed is highly overall correlated with Opp Pass TDs Allowed and 2 other fieldsHigh correlation
Pass Yards Per Game Allowed is highly overall correlated with Opp Pass TDs Allowed and 1 other fieldsHigh correlation
Opp Redzone Scores is highly overall correlated with Opp Rush Touchdowns AllowedHigh correlation
Yds/Rush Allowed is highly overall correlated with Opp Rush Touchdowns Allowed and 1 other fieldsHigh correlation
Opp Rush Touchdowns Allowed is highly overall correlated with Opp Redzone Scores and 1 other fieldsHigh correlation
Opponents Intercepted is highly overall correlated with WinsHigh correlation
Wins is highly overall correlated with Yards/Pass Attempt Allowed and 2 other fieldsHigh correlation
Wins has 14 (1.1%) zerosZeros
Opp.Pass.TDs.Allowed has 102 (8.3%) zerosZeros

Reproduction

Analysis started2023-10-17 17:44:16.835185
Analysis finished2023-10-17 17:44:58.903998
Duration42.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Opp Pass TDs Allowed
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.467912
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:44:59.137711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q115.5
median19
Q323
95-th percentile29
Maximum42
Range40
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.8956014
Coefficient of variation (CV)0.30283686
Kurtosis-0.0051174353
Mean19.467912
Median Absolute Deviation (MAD)4
Skewness0.16370336
Sum23965
Variance34.758116
MonotonicityNot monotonic
2023-10-17T13:44:59.500755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
18 90
 
7.3%
17 88
 
7.1%
19 81
 
6.6%
20 79
 
6.4%
16 76
 
6.2%
22 71
 
5.8%
23 69
 
5.6%
24 66
 
5.4%
15 65
 
5.3%
21 63
 
5.1%
Other values (27) 483
39.2%
ValueCountFrequency (%)
2 1
 
0.1%
3 1
 
0.1%
5 4
 
0.3%
6 8
 
0.6%
7 7
 
0.6%
8 15
1.2%
9 14
1.1%
10 24
1.9%
11 32
2.6%
12 34
2.8%
ValueCountFrequency (%)
42 1
 
0.1%
40 1
 
0.1%
37 1
 
0.1%
36 1
 
0.1%
35 3
 
0.2%
34 4
 
0.3%
33 10
0.8%
32 11
0.9%
31 16
1.3%
30 10
0.8%

Yards/Pass Attempt Allowed
Real number (ℝ)

HIGH CORRELATION 

Distinct368
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.231576
Minimum4.96
Maximum10.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:44:59.942266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.96
5-th percentile5.83
Q16.64
median7.19
Q37.73
95-th percentile8.865
Maximum10.69
Range5.73
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation0.89017617
Coefficient of variation (CV)0.12309574
Kurtosis0.37065696
Mean7.231576
Median Absolute Deviation (MAD)0.55
Skewness0.4769862
Sum8902.07
Variance0.79241361
MonotonicityNot monotonic
2023-10-17T13:45:00.342797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.76 12
 
1.0%
7.13 11
 
0.9%
7.25 10
 
0.8%
7.35 10
 
0.8%
6.95 10
 
0.8%
6.94 10
 
0.8%
6.73 10
 
0.8%
7.24 10
 
0.8%
7.55 9
 
0.7%
6.65 9
 
0.7%
Other values (358) 1130
91.8%
ValueCountFrequency (%)
4.96 1
0.1%
5.14 1
0.1%
5.18 1
0.1%
5.27 2
0.2%
5.38 1
0.1%
5.41 1
0.1%
5.43 1
0.1%
5.44 1
0.1%
5.45 1
0.1%
5.47 1
0.1%
ValueCountFrequency (%)
10.69 1
0.1%
10.42 1
0.1%
10.21 1
0.1%
10 1
0.1%
9.99 1
0.1%
9.92 1
0.1%
9.89 1
0.1%
9.78 1
0.1%
9.76 1
0.1%
9.74 2
0.2%

Pass Yards Per Game Allowed
Real number (ℝ)

HIGH CORRELATION 

Distinct784
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.28692
Minimum135.6
Maximum367.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:00.754391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum135.6
5-th percentile173.2
Q1204.45
median227.2
Q3251.5
95-th percentile286.35
Maximum367.2
Range231.6
Interquartile range (IQR)47.05

Descriptive statistics

Standard deviation34.378958
Coefficient of variation (CV)0.15059539
Kurtosis0.16353173
Mean228.28692
Median Absolute Deviation (MAD)23.6
Skewness0.27681839
Sum281021.2
Variance1181.9128
MonotonicityNot monotonic
2023-10-17T13:45:01.150510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237.8 7
 
0.6%
206.8 5
 
0.4%
224.3 5
 
0.4%
219.8 5
 
0.4%
212.4 5
 
0.4%
216.5 5
 
0.4%
215.8 4
 
0.3%
200.8 4
 
0.3%
219.5 4
 
0.3%
229.8 4
 
0.3%
Other values (774) 1183
96.1%
ValueCountFrequency (%)
135.6 1
0.1%
142.5 1
0.1%
143.3 1
0.1%
147.8 1
0.1%
148.5 1
0.1%
149.9 1
0.1%
150.1 1
0.1%
150.2 1
0.1%
150.7 1
0.1%
152.1 1
0.1%
ValueCountFrequency (%)
367.2 1
0.1%
357.4 1
0.1%
341 1
0.1%
337.5 1
0.1%
333.9 1
0.1%
333.2 1
0.1%
324.8 1
0.1%
324.4 1
0.1%
323 1
0.1%
321.4 1
0.1%

Opp Redzone Scores
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.235581
Minimum6
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:01.555584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21
Q131
median37
Q343
95-th percentile53
Maximum65
Range59
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.6819202
Coefficient of variation (CV)0.26001797
Kurtosis0.11499014
Mean37.235581
Median Absolute Deviation (MAD)6
Skewness0.00078843887
Sum45837
Variance93.739579
MonotonicityNot monotonic
2023-10-17T13:45:01.922009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 65
 
5.3%
36 59
 
4.8%
37 55
 
4.5%
38 53
 
4.3%
39 53
 
4.3%
41 51
 
4.1%
40 50
 
4.1%
34 50
 
4.1%
32 47
 
3.8%
43 41
 
3.3%
Other values (47) 707
57.4%
ValueCountFrequency (%)
6 1
 
0.1%
8 2
 
0.2%
9 2
 
0.2%
11 2
 
0.2%
12 3
 
0.2%
14 2
 
0.2%
15 5
0.4%
16 8
0.6%
17 5
0.4%
18 2
 
0.2%
ValueCountFrequency (%)
65 1
 
0.1%
64 3
0.2%
63 2
 
0.2%
62 4
0.3%
61 5
0.4%
60 3
0.2%
59 6
0.5%
58 6
0.5%
57 2
 
0.2%
56 4
0.3%

Yds/Rush Allowed
Real number (ℝ)

HIGH CORRELATION 

Distinct328
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2716084
Minimum2.01
Maximum7.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:02.302875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.01
5-th percentile3.13
Q13.74
median4.22
Q34.77
95-th percentile5.61
Maximum7.67
Range5.66
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation0.77981894
Coefficient of variation (CV)0.18255862
Kurtosis0.27415135
Mean4.2716084
Median Absolute Deviation (MAD)0.52
Skewness0.37298719
Sum5258.35
Variance0.60811757
MonotonicityNot monotonic
2023-10-17T13:45:02.678956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.14 13
 
1.1%
4.34 13
 
1.1%
4.32 13
 
1.1%
3.7 11
 
0.9%
3.77 10
 
0.8%
3.81 10
 
0.8%
4.23 10
 
0.8%
4.77 10
 
0.8%
4.55 10
 
0.8%
4.24 10
 
0.8%
Other values (318) 1121
91.1%
ValueCountFrequency (%)
2.01 1
0.1%
2.12 1
0.1%
2.27 1
0.1%
2.39 1
0.1%
2.43 1
0.1%
2.47 2
0.2%
2.51 1
0.1%
2.55 1
0.1%
2.56 1
0.1%
2.58 2
0.2%
ValueCountFrequency (%)
7.67 1
0.1%
6.86 1
0.1%
6.78 1
0.1%
6.56 1
0.1%
6.54 1
0.1%
6.5 1
0.1%
6.47 1
0.1%
6.46 1
0.1%
6.42 1
0.1%
6.35 1
0.1%

Opp Rush Touchdowns Allowed
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.971568
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:03.066921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q115
median19
Q325
95-th percentile34
Maximum50
Range49
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7496396
Coefficient of variation (CV)0.38803361
Kurtosis0.2110897
Mean19.971568
Median Absolute Deviation (MAD)5
Skewness0.49131107
Sum24585
Variance60.056915
MonotonicityNot monotonic
2023-10-17T13:45:03.470919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
19 87
 
7.1%
17 77
 
6.3%
21 65
 
5.3%
18 64
 
5.2%
16 62
 
5.0%
15 60
 
4.9%
14 57
 
4.6%
24 56
 
4.5%
20 55
 
4.5%
13 50
 
4.1%
Other values (37) 598
48.6%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 2
 
0.2%
4 4
 
0.3%
5 9
 
0.7%
6 10
 
0.8%
7 21
1.7%
8 23
1.9%
9 27
2.2%
10 26
2.1%
ValueCountFrequency (%)
50 1
 
0.1%
49 1
 
0.1%
45 2
 
0.2%
44 1
 
0.1%
43 2
 
0.2%
42 2
 
0.2%
41 1
 
0.1%
40 6
0.5%
39 5
0.4%
38 6
0.5%

Average Sacks per Game
Real number (ℝ)

Distinct172
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.115792
Minimum0.08
Maximum5.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:03.857341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile1
Q11.57
median2.08
Q32.58
95-th percentile3.4
Maximum5.25
Range5.17
Interquartile range (IQR)1.01

Descriptive statistics

Standard deviation0.76705328
Coefficient of variation (CV)0.36253718
Kurtosis0.40354751
Mean2.115792
Median Absolute Deviation (MAD)0.5
Skewness0.40239125
Sum2604.54
Variance0.58837073
MonotonicityNot monotonic
2023-10-17T13:45:04.250855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 52
 
4.2%
2.08 49
 
4.0%
1.92 45
 
3.7%
3 36
 
2.9%
2.33 33
 
2.7%
1.62 27
 
2.2%
1 26
 
2.1%
2.92 24
 
1.9%
2.42 24
 
1.9%
2.5 24
 
1.9%
Other values (162) 891
72.4%
ValueCountFrequency (%)
0.08 1
0.1%
0.14 1
0.1%
0.23 1
0.1%
0.25 1
0.1%
0.31 1
0.1%
0.33 1
0.1%
0.38 1
0.1%
0.42 1
0.1%
0.44 2
0.2%
0.5 1
0.1%
ValueCountFrequency (%)
5.25 1
 
0.1%
5.22 1
 
0.1%
4.92 1
 
0.1%
4.83 2
 
0.2%
4.58 1
 
0.1%
4.42 1
 
0.1%
4.33 3
0.2%
4.17 3
0.2%
4 6
0.5%
3.92 1
 
0.1%

Opponents Intercepted
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.85459
Minimum0
Maximum26
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:04.589828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median11
Q314
95-th percentile18.5
Maximum26
Range26
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4554681
Coefficient of variation (CV)0.41046859
Kurtosis0.11606396
Mean10.85459
Median Absolute Deviation (MAD)3
Skewness0.34211798
Sum13362
Variance19.851196
MonotonicityNot monotonic
2023-10-17T13:45:04.907271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
10 109
 
8.9%
9 107
 
8.7%
12 106
 
8.6%
11 102
 
8.3%
8 95
 
7.7%
13 93
 
7.6%
14 84
 
6.8%
6 77
 
6.3%
7 76
 
6.2%
15 72
 
5.8%
Other values (17) 310
25.2%
ValueCountFrequency (%)
0 3
 
0.2%
1 2
 
0.2%
2 18
 
1.5%
3 29
 
2.4%
4 30
 
2.4%
5 54
4.4%
6 77
6.3%
7 76
6.2%
8 95
7.7%
9 107
8.7%
ValueCountFrequency (%)
26 5
 
0.4%
25 3
 
0.2%
24 1
 
0.1%
23 1
 
0.1%
22 8
 
0.6%
21 8
 
0.6%
20 13
 
1.1%
19 23
1.9%
18 29
2.4%
17 38
3.1%

Wins
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.506905
Minimum0
Maximum15
Zeros14
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:05.218286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q39
95-th percentile12
Maximum15
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1325321
Coefficient of variation (CV)0.4814166
Kurtosis-0.68649632
Mean6.506905
Median Absolute Deviation (MAD)2
Skewness0.091585505
Sum8010
Variance9.8127572
MonotonicityNot monotonic
2023-10-17T13:45:05.490191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 150
12.2%
8 149
12.1%
5 120
9.7%
3 119
9.7%
4 113
9.2%
6 107
8.7%
9 106
8.6%
10 99
8.0%
2 80
6.5%
11 69
5.6%
Other values (6) 119
9.7%
ValueCountFrequency (%)
0 14
 
1.1%
1 41
 
3.3%
2 80
6.5%
3 119
9.7%
4 113
9.2%
5 120
9.7%
6 107
8.7%
7 150
12.2%
8 149
12.1%
9 106
8.6%
ValueCountFrequency (%)
15 3
 
0.2%
14 9
 
0.7%
13 18
 
1.5%
12 34
 
2.8%
11 69
5.6%
10 99
8.0%
9 106
8.6%
8 149
12.1%
7 150
12.2%
6 107
8.7%

Opp.Redzone.Scores
Real number (ℝ)

Distinct313
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.671354
Minimum0.5
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:05.853492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.7425
Q10.837
median0.951
Q341
95-th percentile68.5
Maximum130
Range129.5
Interquartile range (IQR)40.163

Descriptive statistics

Standard deviation27.042143
Coefficient of variation (CV)1.192789
Kurtosis1.7996543
Mean22.671354
Median Absolute Deviation (MAD)0.259
Skewness1.2884922
Sum27908.437
Variance731.27749
MonotonicityNot monotonic
2023-10-17T13:45:06.239712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 27
 
2.2%
35 26
 
2.1%
41 25
 
2.0%
43 23
 
1.9%
45 22
 
1.8%
37 22
 
1.8%
34 21
 
1.7%
40 21
 
1.7%
44 20
 
1.6%
42 20
 
1.6%
Other values (303) 1004
81.6%
ValueCountFrequency (%)
0.5 1
 
0.1%
0.593 1
 
0.1%
0.6 1
 
0.1%
0.615 1
 
0.1%
0.64 1
 
0.1%
0.647 1
 
0.1%
0.655 1
 
0.1%
0.656 1
 
0.1%
0.657 1
 
0.1%
0.667 3
0.2%
ValueCountFrequency (%)
130 1
0.1%
129 1
0.1%
127 2
0.2%
126 1
0.1%
125 1
0.1%
124 1
0.1%
123 1
0.1%
121 2
0.2%
120 1
0.1%
119 1
0.1%

Opp.Pass.TDs.Allowed
Real number (ℝ)

ZEROS 

Distinct179
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.533875
Minimum0
Maximum433.7
Zeros102
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size19.2 KiB
2023-10-17T13:45:06.647855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median19
Q327
95-th percentile238.6
Maximum433.7
Range433.7
Interquartile range (IQR)14

Descriptive statistics

Standard deviation69.811604
Coefficient of variation (CV)1.7223027
Kurtosis7.2124987
Mean40.533875
Median Absolute Deviation (MAD)7
Skewness2.8488009
Sum49897.2
Variance4873.66
MonotonicityNot monotonic
2023-10-17T13:45:07.013771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102
 
8.3%
19 64
 
5.2%
18 62
 
5.0%
20 52
 
4.2%
17 52
 
4.2%
14 50
 
4.1%
22 47
 
3.8%
24 44
 
3.6%
15 43
 
3.5%
16 43
 
3.5%
Other values (169) 672
54.6%
ValueCountFrequency (%)
0 102
8.3%
1 31
 
2.5%
2 2
 
0.2%
3 1
 
0.1%
4 5
 
0.4%
5 7
 
0.6%
6 13
 
1.1%
7 14
 
1.1%
8 12
 
1.0%
9 24
 
1.9%
ValueCountFrequency (%)
433.7 1
0.1%
392.6 1
0.1%
380.9 1
0.1%
378.3 1
0.1%
355.3 1
0.1%
347 1
0.1%
338.2 1
0.1%
337.4 1
0.1%
326.7 1
0.1%
321.2 1
0.1%

Interactions

2023-10-17T13:44:54.460127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:17.350035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:21.055433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:24.713613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:28.033314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:31.708930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:35.235480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:38.854182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:43.468251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:47.085841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:50.764732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:54.794347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:17.687310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:21.411780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:25.048636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:28.414659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:32.052006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:35.557486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:39.211767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:43.825999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:47.405131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:51.120812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:55.112852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:18.010965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:21.718775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:25.377854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:28.736519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:32.377985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:35.891092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:39.525997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:44.155377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:47.753844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:51.447444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:55.418189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:18.351616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:22.037838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:25.681333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:29.066709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:32.687954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:36.209924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:39.850332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:44.456985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:48.072653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:51.774718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:55.746699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:18.710204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:22.358024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:25.993149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:29.380258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:32.974641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:36.522856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:40.174715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:44.823439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:48.400618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:52.096039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:56.043369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:19.039909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:22.706200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:26.302217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:29.740327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:33.286203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:36.855148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:40.513244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:45.148161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:48.740085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:52.397254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:56.370123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:19.382020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:23.054645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:26.538969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:30.077935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:33.625994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:37.190822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:40.852562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:45.512793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:49.086903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:52.764547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:56.700204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:19.721648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:23.396348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:26.776002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:30.408266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:33.937752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:37.515358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:41.153808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:45.840886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:49.428735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:53.106133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:57.041141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:20.055738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:23.742606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:27.077492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:30.723124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:34.270565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:37.843653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:41.452573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:46.145385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:49.766197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:53.433076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:57.376328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:20.405142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:24.070024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:27.392667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:31.055954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:34.592190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:38.198132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:41.777301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:46.466942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:50.102668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:53.787222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:57.694027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:20.739987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:24.401551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:27.698670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:31.372915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:34.922692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:38.544421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:42.105734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:46.786028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:50.442055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-17T13:44:54.126925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-10-17T13:45:07.345760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Opp Pass TDs AllowedYards/Pass Attempt AllowedPass Yards Per Game AllowedOpp Redzone ScoresYds/Rush AllowedOpp Rush Touchdowns AllowedAverage Sacks per GameOpponents InterceptedWinsOpp.Redzone.ScoresOpp.Pass.TDs.Allowed
Opp Pass TDs Allowed1.0000.5830.6310.4620.2910.3370.089-0.111-0.191-0.0370.003
Yards/Pass Attempt Allowed0.5831.0000.6300.3000.4880.4910.180-0.418-0.527-0.027-0.006
Pass Yards Per Game Allowed0.6310.6301.0000.3540.1710.2550.085-0.100-0.2020.015-0.026
Opp Redzone Scores0.4620.3000.3541.0000.3500.5290.073-0.002-0.058-0.047-0.015
Yds/Rush Allowed0.2910.4880.1710.3501.0000.7410.196-0.358-0.557-0.0180.014
Opp Rush Touchdowns Allowed0.3370.4910.2550.5290.7411.0000.195-0.214-0.407-0.042-0.005
Average Sacks per Game0.0890.1800.0850.0730.1960.1951.000-0.132-0.376-0.007-0.053
Opponents Intercepted-0.111-0.418-0.100-0.002-0.358-0.214-0.1321.0000.5550.018-0.058
Wins-0.191-0.527-0.202-0.058-0.557-0.407-0.3760.5551.000-0.0030.013
Opp.Redzone.Scores-0.037-0.0270.015-0.047-0.018-0.042-0.0070.018-0.0031.0000.104
Opp.Pass.TDs.Allowed0.003-0.006-0.026-0.0150.014-0.005-0.053-0.0580.0130.1041.000

Missing values

2023-10-17T13:44:58.129869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-17T13:44:58.639115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Opp Pass TDs AllowedYards/Pass Attempt AllowedPass Yards Per Game AllowedOpp Redzone ScoresYds/Rush AllowedOpp Rush Touchdowns AllowedAverage Sacks per GameOpponents InterceptedWinsOpp.Redzone.ScoresOpp.Pass.TDs.Allowed
Opponent 3rd Percent
0.279107.11159.1213.39100.9291043.027.0
0.352228.24243.8454.26254.589217.013.0
0.305125.63187.8333.64151.6971134.020.0
0.423187.24219.7293.64201.507640.024.0
0.478257.74258.6505.59331.923537.025.0
0.494146.90233.4505.19312.3311344.020.0
0.450248.53294.7474.55262.389744.019.0
0.364258.30242.7334.03233.176338.019.0
0.387156.98160.0335.05191.0010631.014.0
0.425156.48222.6414.64282.676532.023.0
Opp Pass TDs AllowedYards/Pass Attempt AllowedPass Yards Per Game AllowedOpp Redzone ScoresYds/Rush AllowedOpp Rush Touchdowns AllowedAverage Sacks per GameOpponents InterceptedWinsOpp.Redzone.ScoresOpp.Pass.TDs.Allowed
Opponent 3rd Percent
0.368185.99206.9404.18192.5418942.00.0
0.320197.24232.9414.36282.0010243.00.0
0.297126.22172.7233.21132.5419849.00.0
0.363206.42223.0373.73132.0812445.01.0
0.378155.83227.2384.13202.3116944.00.0
0.381237.67270.8414.45252.4616641.00.0
0.427247.88263.3504.43232.3312468.00.0
0.451106.84177.9374.29241.6714844.00.0
0.457166.61169.2435.38301.838134.00.0
0.306166.20202.5253.2281.239929.01.0